Introduction into Decision Support Systems. Gastcollege MMI Slinger Jansen en Siamak Farshidi

Transcription

1 Introduction into Decision Support Systems Gastcollege MMI Slinger Jansen en Siamak Farshidi

2 Where shall we go for dinner tonight?

3 So if we decide now, do we have the optimal decision?

4 Where shall we go for dinner tonight? Food preferences Allergies Dietary restrictions Location Parking Able to accept large groups Schedules Atmosphere Price

5 But can DSSs make poor decisions? We gebruiken ingrediënten van hoge kwaliteit, die met zorg zijn geselecteerd. We werken nauw samen met onze leveranciers om ervoor te zorgen dat we altijd voldoen aan de hoogste kwaliteitsstandaarden. In onze restaurants bereiden we alle producten volgens strenge regels en procedures. Onze medewerkers krijgen daarvoor een uitgebreide training.

6 What is a decision?

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8 Management decisions/problems Most management problems for which decisions are sought can be represented by three standard elements objectives, decision variables, and constraints. These problems can be structured, semi-structured and unstructured in nature: Objective Maximize profit Provide earliest entry into market Minimize employee discomfort/turnover Decision variables Determine what price to use Determine length of time tests should be run on a new product/service Determine the responsibilities to assign to each worker Constraints Can t charge below cost Test enough to meet minimum safety regulations Ensure responsibilities are at most shared by two workers

9 Information, Decisions, and Management Information o Type of information required is directly related to the level of management and the amount of structure in the decision situation Levels of managerial decision-making o Strategic Management (2-5yrs) o Tactical Management (.5-2yrs) o Operational Management (What are we doing today mommy?)

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11 What is a decision support system? An interactive computer-based system or subsystem intended to help decision makers- use information systems (data, computational processing and output (report)) - identify and solve problems and make decisions. Decision Support System is a general term for any computer application that enhances a person or group s ability to make decisions; can be as simple as an excel spread sheet to a complicated system involving large databases, statictical modelling techniques and applying A.I. to dervive information.

12 Problem Recognition Implementation Problem Definition Alternative Generation Choice Model Development Alternative Analysis

13 Information Systems to support decisions Decision support provided Information form and frequency Information format Information processing methodology Management Information Systems Provide information about the performance of the organization Periodic and exception reports Prespecified, fixed format Information produced by extraction and manipulation (basic analysis) of business data Decision Support Systems Provide information and techniques to analyze specific problems Interactive enquiries and responses Ad hoc, flexible, and adaptable format Information produced by analytical modeling of business data (see next lecture)

14 DSS for Customer Relationship Management Uses data mining to guide decisions Consolidates customer information into massive data warehouses Uses various analytical tools to slice information into small segments; e.g. using OLAP software

15 DSS for customer analysis and segmentation

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17 Achieving Success with Analytics Optimization Modeling Predictive Modeling $ROI Standard Reports Raw Data Ad Hoc Reports and OLAP Descriptive Modeling What will happen? Why did it happen? What is the best that could happen? What happened? Data Information Intelligence Decision Support Decision Guidance

18 History of DSS

19 OLAP Online Analytical Processing (OLAP) o A capability of management, decision support, and executive information systems that enables managers and analysts to interactively examine and manipulate large amounts of detailed and consolidated data from many perspectives Basic analytical operations include o Consolidation: aggregation of data o Drill-Down: display detail data o Slicing & Dicing: produce different views from database

20 Decision Support Technologies Management Information Systems (MIS) Decision Support Systems (DSS) Enterprise (Executive) Information Systems (EIS) Enterprise Resource Planning (ERP) and Supply-Chain Management (SCM) Knowledge Management Systems Expert Systems (ES) Artificial Neural Networks (ANN)

21 Decision Support Systems Computer-based information systems that provide interactive information support to managers during the decision-making process DSS use: o Analytical models o Specialized databases o Decision makers own insights and judgements o Interactive, computer-based modeling processes to support the making of semistructured and unstructured decisions by individual managers o Data mining èanalysis of large pools of data to find patterns and rules that can be used to guide decision making and predict future behavior

22 Decision Support Systems Using a DSS involves four basic types of modeling activities: o What-if Analysisè an end user makes changes to variables, or relationships among variables, and observes the resulting changes in the value of other variables o Sensitivitiy Analysisè a special case of what-if analysis the value of only one variable is changed repeatedly, and the resulting changes on other variables are observed. o Goal Seeking Analysisè sets a target value for a variable and then repeatedly changes other variables until the target value is achieved o Optimization Analysisè the goal is to find the optimum value for one or more target variables, given certain constraints

23 Artificial Intelligence Concepts Expert systems Human knowledge stored on machine for use in problemsolving Natural language processing Allows user to use native language instead of English Speech recognition Computer understanding spoken language Sensory systems Vision, tactile, and signal processing systems Robotics Sensory systems combine with programmable electromechanical device to perform manual labor 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-23

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25 Experts Experts Have special knowledge, judgment, and experience Can apply these to solve problems Higher performance level than average person Relative Faster solutions Recognize patterns Expertise Task specific knowledge of experts Acquired from reading, training, practice 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-25

26 Expert Systems Features Expertise Capable of making expert level decisions Symbolic reasoning Knowledge represented symbolically Reasoning mechanism symbolic Deep knowledge Knowledge base contains complex knowledge Self-knowledge Able to examine own reasoning Explain why conclusion reached 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-26

27 Applications Finance Insurance evaluation, credit analysis, tax planning, financial planning and reporting, performance evaluation Data processing Systems planning, equipment maintenance, vendor evaluation, network management Marketing Customer-relationship management, market analysis, product planning Human resources HR planning, performance evaluation, scheduling, pension management, legal advising Manufacturing Production planning, quality management, product design, plant site selection, equipment maintenance and repair 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-27

28 Major Components of Expert Systems Major components Knowledge base Facts Special heuristics to direct use of knowledge Inference engine Brain Control structure Rule interpreter User interface Language processor 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-28

29 Additional Components of Expert Systems Additional components Knowledge acquisition subsystem Accumulates, transfers, and transforms expertise to computer Workplace Blackboard Area of working memory Decisions Plan, agenda, solution Justifier Explanation subsystem Traces responsibility for conclusions Knowledge refinement system Analyzes knowledge and use for learning and improvements 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-29

30 Benefits of Expert Systems Ability to work with incomplete, imprecise, uncertain data Provides training Enhanced problem solving and decision-making Rapid feedback Facilitate communications Reliable decision quality Ability to solve complex problems Ease of knowledge transfer to remote locations Provides intelligent capabilities to other information systems 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-30

31 Limitations Knowledge not always readily available Difficult to extract expertise from humans Approaches vary Natural cognitive limitations Vocabulary limited Wrong recommendations Lack of end-user trust Knowledge subject to biases Systems may not be able to arrive at conclusions 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-31

32 Success Factors Management champion User involvement Training Expertise from cooperative experts Qualitative, not quantitative, problem User-friendly interface Expert s level of knowledge must be high 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 10-32

33 Categories of Mathematical Models Model Independent OR/MS Category Form of f(. ) Variables Techniques Prescriptive known, known or under LP, Networks, IP, well-defined decision maker s CPM, EOQ, NLP, control GP, MOLP Predictive unknown, known or under Regression Analysis, ill-defined decision maker s Time Series Analysis, control Discriminant Analysis Descriptive known, unknown or Simulation, PERT, well-defined uncertain Queueing, Inventory Models

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